Relevancy of data is of increasing importance in modern systems. For example, search engines and ad matching engines may seek the most relevant data to present to a user. However, traditional techniques utilized for identifying relevant data have generally exhibited various limitations. Just by way of example, the opinion and behavior of users are often used by such traditional techniques in order to determine the relevancy of data. Thus, if more users select particular search results received from a search engine as opposed to other search results, the selected search results may be identified as more relevant than the other search results.
Unfortunately, techniques for selecting which users to learn from have conventionally been inflexible. For example, the manner in which relevant users are identified has been limited, thus further limiting the amount of information utilized in identifying relevant data. There is thus a need for addressing these and/or other issues associated with the prior art.